Generative AI: A Game-Changer in Age-Related Macular Degeneration Screening by Achieving ‘Noninvasive’ Indocyanine Green Angiography

Generative AI: A Game-Changer in Age-Related Macular Degeneration Screening by Achieving ‘Noninvasive’ Indocyanine Green Angiography
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Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening - npj Digital Medicine

Age-related macular degeneration (AMD) is the leading cause of central vision impairment among the elderly. Effective and accurate AMD screening tools are urgently needed. Indocyanine green angiography (ICGA) is a well-established technique for detecting chorioretinal diseases, but its invasive nature and potential risks impede its routine clinical application. Here, we innovatively developed a deep-learning model capable of generating realistic ICGA images from color fundus photography (CF) using generative adversarial networks (GANs) and evaluated its performance in AMD classification. The model was developed with 99,002 CF-ICGA pairs from a tertiary center. The quality of the generated ICGA images underwent objective evaluation using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity measures (SSIM), etc., and subjective evaluation by two experienced ophthalmologists. The model generated realistic early, mid and late-phase ICGA images, with SSIM spanned from 0.57 to 0.65. The subjective quality scores ranged from 1.46 to 2.74 on the five-point scale (1 refers to the real ICGA image quality, Kappa 0.79–0.84). Moreover, we assessed the application of translated ICGA images in AMD screening on an external dataset (n = 13887) by calculating area under the ROC curve (AUC) in classifying AMD. Combining generated ICGA with real CF images improved the accuracy of AMD classification with AUC increased from 0.93 to 0.97 (P < 0.001). These results suggested that CF-to-ICGA translation can serve as a cross-modal data augmentation method to address the data hunger often encountered in deep-learning research, and as a promising add-on for population-based AMD screening. Real-world validation is warranted before clinical usage.

Drawbacks and Challenges of Applying Unimodal Imaging for AMD Detection

Age-related macular degeneration (AMD) is the leading cause of central vision loss in the aging population. Color fundus photography (CF) is widely applied for AMD screening due to its simplicity and low cost, particularly in source limited area. But CF images have limitations in detecting and distinguishing certain lesions, because of the unstable image quality and common characteristics shared by several chorioretinal diseases on CF images. Indocyanine green angiography (ICGA) is another well-established fundus imaging technique for screening chorioretinal conditions, owning its unique advantages in visualizing deeper choroidal vasculature and lesions behind retinal pigment epithelium. However, ICGA is an invasive imaging modality with potential adverse reactions. Besides, the complex operating procedures impede its widespread implementation in clinical settings.

Leverage the Power of GANs: Providing ‘Noninvasive’ ICGA to Enable Multimodal AMD Screening

To harness the benefits of different imaging modalities and achieve efficient multimodal AMD screening, we innovatively developed a cross-modality translation model capable of synthesizing realistic ICGA images from non-invasive and easily accessible CF images using generative adversarial networks (GANs) (Figure 1). The algorithm showed high authenticity in generating anatomical structures and pathological lesions in both internal and external datasets (Figure 2). Additionally, the integration of translated ICGA images with real CF images significantly improves the accuracy of AMD screening and effectively reduce classification errors in external validation (Table 1).

Figure 1. Flow chart of the study. GAN=generative adversarial networks, CF=color fundus photography, ICGA=indocyanine green angiography, AMD=age-related macular degeneration, MAE=mean absolute error, PSNR= peak signal-to-noise ratio, SSIM= structural similarity measures, MS-SSIM=multi-scale structural similarity measures.

Figure 2. Examples of real and translated indocyanine green angiography (ICGA). 1st row, early dry AMD, 2nd row, intermediate dry AMD, 3rd row, wet AMD, 4th row, wet AMD. 1-3 rows: internal test set, 4th row: external test set.

Table 1. Age-related macular degeneration (AMD) classification based on color fundus photography (CF) and CF+ translated indocyanine green angiography (ICGA) images on the AMD dataset (n=13887)

 

F1-score

Sensitivity

Specificity

Accuracy

AUC

P value

CF

0.8386

0.8368

0.9323

0.8368

0.9312

 

CF+early

0.8601

0.8598

0.9428

0.8598

0.9407

0.4400

CF+early+mid

0.8854

0.8850

0.9466

0.8850

0.9632

<0.0001*

CF+early+mid+late

0.8875

0.8872

0.9474

0.8872

0.9688

<0.0001*

Conclusion and future work

Our study pioneeringly established the feasibility of generating ICGA using CF, and introduced a cross-modality approach to augment data for AMD-related deep learning research. Furthermore, our findings highlight the potential of the CF-to-ICGA model as a valuable approach for evaluating multiple chorioretinal diseases accurately and non-invasively. Further prospective trials are required to translate this research discovery into clinical benefit in real-world practice.

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Medical and Health Technologies
Life Sciences > Health Sciences > Clinical Medicine > Medical and Health Technologies
Health Care Management
Humanities and Social Sciences > Business and Management > Industries > Health Care Management
Ophthalmology
Life Sciences > Health Sciences > Clinical Medicine > Ophthalmology
Macular degeneration
Life Sciences > Health Sciences > Clinical Medicine > Diseases > Eye Diseases > Macular degeneration
Fluorescence Imaging
Life Sciences > Biological Sciences > Biological Techniques > Biological Imaging > Fluorescence Imaging
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